{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "from statsmodels.tsa import stattools\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Set the current working directory\n",
    "os.chdir('D:/Practical Time Series/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read data from Excel file\n",
    "daily_temp = pd.read_excel('datasets/mean-daily-temperature-fisher-river.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean_Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988-01-02</td>\n",
       "      <td>-20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988-01-03</td>\n",
       "      <td>-22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1988-01-04</td>\n",
       "      <td>-30.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>-31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1988-01-06</td>\n",
       "      <td>-27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1988-01-07</td>\n",
       "      <td>-26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1988-01-08</td>\n",
       "      <td>-26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988-01-09</td>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1988-01-10</td>\n",
       "      <td>-23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Mean_Temperature\n",
       "0 1988-01-01            -23.00\n",
       "1 1988-01-02            -20.50\n",
       "2 1988-01-03            -22.00\n",
       "3 1988-01-04            -30.50\n",
       "4 1988-01-05            -31.00\n",
       "5 1988-01-06            -27.50\n",
       "6 1988-01-07            -26.25\n",
       "7 1988-01-08            -26.50\n",
       "8 1988-01-09            -23.00\n",
       "9 1988-01-10            -23.50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display first 20 rows of the DataFrame\n",
    "daily_temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make formatted date as the row index of the dataset and drop the Date column\n",
    "daily_temp.index = daily_temp['Date'].map(lambda date: pd.to_datetime(date, '%Y-%m-%d'))\n",
    "daily_temp.drop('Date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean_Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-02</th>\n",
       "      <td>-20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-03</th>\n",
       "      <td>-22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-04</th>\n",
       "      <td>-30.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-05</th>\n",
       "      <td>-31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-06</th>\n",
       "      <td>-27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-07</th>\n",
       "      <td>-26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-08</th>\n",
       "      <td>-26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-09</th>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-10</th>\n",
       "      <td>-23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mean_Temperature\n",
       "Date                        \n",
       "1988-01-01            -23.00\n",
       "1988-01-02            -20.50\n",
       "1988-01-03            -22.00\n",
       "1988-01-04            -30.50\n",
       "1988-01-05            -31.00\n",
       "1988-01-06            -27.50\n",
       "1988-01-07            -26.25\n",
       "1988-01-08            -26.50\n",
       "1988-01-09            -23.00\n",
       "1988-01-10            -23.50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Re-display the first 10 rows of the modified DataFrame\n",
    "daily_temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TMRgwAHj5Zes/m6gOPhj4v/8LJUmxw3bbyWVmOggZJgLIFG1xcFO0GQrH4sXAm29a/zdt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\n/wK9M6wRQD5GHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76d6750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('Daily temperatures of Fisher River, TX, US')\n",
    "daily_temp.plot(ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_08.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of monthly mean temperature dataset: (48,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1988-01-31   -22.137097\n",
       "1988-02-29   -19.025862\n",
       "1988-03-31    -8.258065\n",
       "1988-04-30     2.641667\n",
       "1988-05-31    11.290323\n",
       "1988-06-30    19.291667\n",
       "1988-07-31    19.048387\n",
       "1988-08-31    17.379032\n",
       "1988-09-30    10.675000\n",
       "1988-10-31     2.467742\n",
       "Freq: M, Name: Mean_Temperature, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate monthly mean temperature\n",
    "montly_resample = daily_temp['Mean_Temperature'].resample('M')\n",
    "monthly_mean_temp = montly_resample.mean()\n",
    "print('Shape of monthly mean temperature dataset:', monthly_mean_temp.shape)\n",
    "monthly_mean_temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8+ZbWtv/+6dTvHm5EqCY/yqySPHecpRVOCMl7WCGtzto8hhXSDCcAjBsHK1Yk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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76d6610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the monthly mean temparature\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('Monthly mean temperatures of Fisher River, TX, US')\n",
    "monthly_mean_temp.plot(ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_09.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zMKSFMDpPnhxf66BA0gYhCX/6nDlwxRUumNxS4sIOO7hzMGpUfPJYDCFZwrQQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7c6ec70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ACF of the monthly mean temparature using pandas.tools.plotting.autocorrelation_plot\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('ACF of monthly mean temperatures of Fisher River, TX, US')\n",
    "autocorrelation_plot(monthly_mean_temp, ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_10.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Take seasonal differences with a period of 12 months on monthly mean temperatures\n",
    "seasonal_diff = monthly_mean_temp.diff(12)\n",
    "seasonal_diff = seasonal_diff[12:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aY3uBhJ3dyh9+OjRkf1czB3nn7W+3ExKtW5f+uQrnUNva7EVNu6+lK2o5VEhe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\nURTFESqoiqIojvj/3HoBWEqu7hUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x77f89d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the seasonal differences\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('Seasonal differences')\n",
    "seasonal_diff.plot(ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_11.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W6QGMM9HWalXdoKrrVfUyVf2NOtvFqMGiRfE8c6jtm69b57zwOEEzbidoVj5r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\nT1V7quoEVX2nqu6pqjcVpTktpZebvlB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7a90bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the seasonal differences\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('ACF of Seasonal differences')\n",
    "autocorrelation_plot(seasonal_diff, ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_12.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis is rejected for lags: (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),)\n"
     ]
    }
   ],
   "source": [
    "#Perform Ljung-Box test on monthly mean temperature to get the p-values\n",
    "#We will use lags of upto 10\n",
    "_, _, _, pval_monthly_mean = stattools.acf(monthly_mean_temp, unbiased=True,\n",
    "                                           nlags=10, qstat=True, alpha=0.05)\n",
    "print('Null hypothesis is rejected for lags:', np.where(pval_monthly_mean<=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis is rejected for lags: (array([], dtype=int32),)\n"
     ]
    }
   ],
   "source": [
    "#Perform Ljung-Box test on monthly mean temperature to get the p-values\n",
    "#We will use lags of upto 10\n",
    "_, _, _, pval_seasonal_diff = stattools.acf(seasonal_diff, unbiased=True,\n",
    "                                            nlags=10, qstat=True, alpha=0.05)\n",
    "print('Null hypothesis is rejected for lags:', np.where(pval_seasonal_diff<=0.05))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
